Abstract Memories of waking-life events are incorporated into dreams, but their incorporation is not uniform across a night of sleep. This study aimed to elucidate ways in which such memory sources vary by sleep stage and time of night. Twenty healthy participants (11 F; 24.1 ± 5.7 years) spent a night in the laboratory and were awakened for dream collection approximately 12 times spread across early, middle, and late periods of sleep, while covering all stages of sleep (N1, N2, N3, REM). In the morning, participants identified and dated associated memories of waking-life events for each dream report, when possible. The incorporation of recent memory sources in dreams was more frequent in N1 and REM than in other sleep stages. The incorporation of distant memories from over a week ago, semantic memories not traceable to a single event, and anticipated future events remained stable throughout sleep. In contrast, the relative proportions of recent versus distant memory sources changed across the night, independently of sleep stage, with late-night dreams in all stages having relatively less recent and more remote memory sources than dreams earlier in the night. Qualitatively, dreams tended to repeat similar themes across the night and in different sleep stages. The present findings clarify the temporal course of memory incorporations in dreams, highlighting a specific connection between time of night and the temporal remoteness of memories. We discuss how dream content may, at least in part, reflect the mechanisms of sleep-dependent memory consolidation.
more »
« less
Stability of nocturnal wake and sleep stages defines central nervous system disorders of hypersomnolence
Abstract Study Objectives We determine if young people with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH) have distinct nocturnal sleep stability phenotypes compared to subjectively sleepy controls. Methods Participants were 5- to 21-year old and drug-naïve or drug free: NT1 (n = 46), NT2 (n = 12), IH (n = 18), and subjectively sleepy controls (n = 48). We compared the following sleep stability measures from polysomnogram recording between each hypersomnolence disorder to subjectively sleepy controls: number of wake and sleep stage bouts, Kaplan–Meier survival curves for wake and sleep stages, and median bout durations. Results Compared to the subjectively sleepy control group, NT1 participants had more bouts of wake and all sleep stages (p ≤ .005) except stage N3. NT1 participants had worse survival of nocturnal wake, stage N2, and rapid eye movement (REM) bouts (p < .005). In the first 8 hours of sleep, NT1 participants had longer stage N1 bouts but shorter REM (all ps < .004). IH participants had a similar number of bouts but better survival of stage N2 bouts (p = .001), and shorter stage N3 bouts in the first 8 hours of sleep (p = .003). In contrast, NT2 participants showed better stage N1 bout survival (p = .006) and longer stage N1 bouts (p = .02). Conclusions NT1, NT2, and IH have unique sleep physiology compared to subjectively sleepy controls, with only NT1 demonstrating clear nocturnal wake and sleep instability. Overall, sleep stability measures may aid in diagnoses and management of these central nervous system disorders of hypersomnolence.
more »
« less
- Award ID(s):
- 1853511
- PAR ID:
- 10252817
- Date Published:
- Journal Name:
- Sleep
- ISSN:
- 0161-8105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Study ObjectivesEvaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. MethodsChildren (n = 82, 61% male, 43.9% black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. Three-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin’s concordance correlation coefficient (CCC), mean absolute error (MAE), and mean absolute percent error (MAPE) assessed agreement with ECG estimated heart rate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. ResultsThe developed scripts are available on Github. For the GT9X, CCC was poor at −0.11 and MAE and MAPE were high at 16.8 (SD = 14.2) beats/minute and 20.4% (SD = 18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD = 9.9) beats/minute and 7.3% (SD = 10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. ConclusionsRaw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e. hardware, software, etc.) of the GT9X’s poor performance.more » « less
-
A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies 36±6 min of REM in one night of recorded sleep, while incorrectly labeling less than 10% of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near 80%), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night’s worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data.more » « less
-
The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.more » « less
-
Abstract Emotional memory bias is a common characteristic of internalizing symptomatology and is enhanced during sleep. The current study employs bifactor S-1 modeling to disentangle depression-specific anhedonia, anxiety-specific anxious arousal, and the common internalizing factor, general distress, and test whether these internalizing symptoms interact with sleep to influence memory for emotional and neutral information. Healthy adults (N= 281) encoded scenes featuring either negative objects (e.g., a vicious looking snake) or neutral objects (e.g., a chipmunk) placed on neutral backgrounds (e.g., an outdoor scene). After a 12-hour period of daytime wakefulness (n= 140) or nocturnal sleep (n= 141), participants judged whether objects and backgrounds were the same, similar, or new compared with what they viewed during encoding. Participants also completed the mini version of the Mood and Anxiety Symptom Questionnaire. Higher anxious arousal predicted worse memory across all stimuli features, but only after a day spent being awake—not following a night of sleep. No significant effects were found for general distress and anhedonia in either the sleep or wake condition. In this study, internalizing symptoms were not associated with enhanced emotional memory. Instead, memory performance specifically in individuals with higher anxious arousal was impaired overall, regardless of emotional valence, but this was only the case when the retention interval spanned wakefulness (i.e., not when it spanned sleep). This suggests that sleep may confer a protective effect on general memory impairments associated with anxiety.more » « less
An official website of the United States government

